A preliminary model of the mangrove ecosystem of Dongzhaigang Bay, Hainan, (China) based on Ecopath and Ecospace
Bibliographic record
Abstract
Introduction Marine and coastal ecosystems are complex, especially because of the connection between their many functional groups and the various habitats provided by subsystems. Method To describe the trophic interactions, energy flows, and spatial distribution of species in the ecosystem of Dongzhaigang Bay, in Hainan, China, 28 functional groups were identified, incorporated in Ecopath and Ecospace models, and used to derive the particle size and spatial distribution of the functional groups. Result The results show that the trophic levels of top predators are lower than in similar ecosystems, but that the ecosystem of Dongzhaigang Bay exhibits a high ecotrophic efficiency, leading to a high fraction of its primary and secondary production being transferred to the higher trophic levels. The particle size distribution (PSD; i.e., log(biomass) vs log(particle weight)) in Dongzhaigang Bay, the first estimated for any mangrove ecosystem, has a slope (-0.80) similar to that of other ecosystems along China’s coast , but does not include the largest size groups. Ecospace allowed a visualization of the functional groups in Dongzhaigang Bay, which can serve as a basis, e.g., for identifying areas requiring special protection. Discussion Though the status of the bay, overall, appears to be better than many of the Chinese coastal ecosystems, further monitoring and data collection are required to deepen the understanding of the interactions within the ecosystems, and the impacts of human activities on Dongzhaigang Bay. The models presented here should help in this undertaking.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".